The purpose of this notebook is to cluster and annotate the cells obtained from patient with id 63.
library(Seurat)
library(ggpubr)
library(tidyverse)
# Paths
path_to_obj <- here::here("results/R_objects/patient_63/2.seurat_object_filtered.rds")
path_to_save <- here::here("results/R_objects/patient_63/3.seurat_annotated.rds")
path_to_save_markers <- here::here("3-clustering_and_annotation/tmp/markers_clusters_63.rds")
# Functions
source(here::here("bin/utils.R"))
# Params
k_param <- 10
min_log2FC <- 0.3
alpha <- 0.001
seurat <- readRDS(path_to_obj)
seurat
## An object of class Seurat
## 13680 features across 983 samples within 1 assay
## Active assay: RNA (13680 features, 0 variable features)
seurat <- NormalizeData(
seurat,
normalization.method = "LogNormalize",
scale.factor = 10000
)
seurat <- FindVariableFeatures(seurat)
LabelPoints(
plot = VariableFeaturePlot(seurat),
points = head(VariableFeatures(seurat), 10),
repel = TRUE
)
seurat <- ScaleData(seurat)
seurat <- RunPCA(seurat)
VizDimLoadings(seurat, dims = 1:2, reduction = "pca")
seurat <- RunUMAP(seurat, reduction = "pca", dims = 1:20)
umap_time_point <- DimPlot(seurat, group.by = "time_point")
umap_tissue <- DimPlot(seurat, group.by = "tissue")
umap_time_point + umap_tissue
As upregulation of cell cycle genes is a hallmark of Richter transformation, we will infer the cell cycle score and phase for each cell:
seurat <- CellCycleScoring(
seurat,
s.features = cc.genes.updated.2019$s.genes,
g2m.features = cc.genes.updated.2019$g2m.genes,
set.ident = FALSE
)
DimPlot(seurat, group.by = "Phase")
umap_s_score <- FeaturePlot(seurat, features = "S.Score") +
scale_color_viridis_c(option = "magma") +
labs(title = "S Score") +
theme(
plot.title = element_text(hjust = 0.5, size = 12, face = "plain"),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
)
umap_g2m_score <- FeaturePlot(seurat, features = "G2M.Score") +
scale_color_viridis_c(option = "magma") +
labs(title = "G2M Score") +
theme(
plot.title = element_text(hjust = 0.5, size = 12, face = "plain"),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
)
umap_cc_combined <- ggpubr::ggarrange(
plotlist = list(umap_s_score, umap_g2m_score),
nrow = 2,
ncol = 1,
common.legend = FALSE
)
umap_cc_combined
seurat <- FindNeighbors(
seurat,
k.param = k_param,
dims = 1:20,
reduction = "pca"
)
seurat <- FindClusters(seurat, resolution = 0.2)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 983
## Number of edges: 19317
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8705
## Number of communities: 2
## Elapsed time: 0 seconds
DimPlot(seurat)
Let us subcluster to find the subpopulation of cycling cells
seurat <- FindSubCluster(
seurat,
cluster = "1",
graph.name = "RNA_snn",
subcluster.name = "subcluster_proliferative",
resolution = 0.3
)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 223
## Number of edges: 4807
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7026
## Number of communities: 2
## Elapsed time: 0 seconds
DimPlot(seurat, group.by = "subcluster_proliferative")
seurat$final_clusters <- seurat$subcluster_proliferative
Idents(seurat) <- "final_clusters"
markers <- FindAllMarkers(seurat, only.pos = TRUE, logfc.threshold = min_log2FC)
markers <- markers %>%
mutate(cluster = as.character(cluster)) %>%
dplyr::filter(p_val_adj < alpha) %>%
arrange(cluster) %>%
group_by(cluster) %>%
arrange(desc(avg_log2FC), .by_group = TRUE)
DT::datatable(markers)
| Cluster | Markers | Annotation |
|---|---|---|
| 0 | CXCR4 | CLL-like |
| 1_0 | TCL1A, BTK, WNT3 | RT-like quiescent |
| 1_1 | TOP2A, PCNA | RT-like proliferative |
seurat$annotation_final <- factor(
seurat$final_clusters,
levels = c("0", "1_0", "1_1"),
)
new_levels_63 <- c("CLL-like", "RT-like quiescent", "RT-like proliferative")
levels(seurat$annotation_final) <- new_levels_63
reordered_levels_63 <- new_levels_63
seurat$annotation_final <- factor(seurat$annotation_final, reordered_levels_63)
Idents(seurat) <- "annotation_final"
# Plot UMAP
cols <- c("gray79", "#f6c7c4", "#6d203f")
names(cols) <- levels(seurat$annotation_final)
umap_annotation <- DimPlot(seurat, pt.size = 1)
col_labels <- c(
"CLL-like" = bquote("CLL-like"),
"RT-like quiescent" = bquote("RT-like quiescent"),
"RT-like proliferative" = bquote("RT-like proliferative")
)
umap_annotation <- umap_annotation +
scale_color_manual(values = cols, breaks = names(cols), labels = col_labels) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
)
umap_annotation
# UMAPs
genes_interest <- c("CXCR4", "TCL1A", "BTK", "WNT3", "TOP2A", "PCNA")
feature_plots <- purrr::map(genes_interest, function(x) {
p <- FeaturePlot(seurat, x, pt.size = 1) +
scale_color_viridis_c(option = "magma")
p
})
feature_plots
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# Dot plots
dot_plot <- DotPlot(seurat, features = rev(genes_interest)) +
coord_flip() +
scale_color_viridis_c(option = "magma") +
scale_y_discrete(breaks = names(col_labels), labels = col_labels) +
theme(
axis.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
legend.title = element_text(size = 12)
)
dot_plot
# Violin plots
vln_plot_s <- seurat@meta.data %>%
ggplot(aes(annotation_final, S.Score)) +
geom_violin(fill = "gray") +
labs(x = "", y = "S Phase Score") +
scale_x_discrete(breaks = names(col_labels), labels = col_labels) +
theme_bw() +
theme(axis.text.x = element_text(color = "black", angle = 45, vjust = 1, hjust = 1, size = 11))
vln_plot_s
vln_plot_g2m <- seurat@meta.data %>%
ggplot(aes(annotation_final, G2M.Score)) +
geom_violin(fill = "gray") +
labs(x = "", y = "G2M Phase Score") +
scale_x_discrete(breaks = names(col_labels), labels = col_labels) +
theme_bw() +
theme(axis.text.x = element_text(color = "black", angle = 45, vjust = 1, hjust = 1, size = 11))
vln_plot_g2m
# Save Seurat object
saveRDS(seurat, path_to_save)
# Save markers
markers$annotation <- factor(markers$cluster)
levels(markers$annotation) <- new_levels_63
markers_list <- purrr::map(levels(markers$annotation), function(x) {
df <- markers[markers$annotation == x, ]
df <- df[, c(7, 1, 5, 2:4, 6, 8)]
df
})
names(markers_list) <- levels(markers$annotation)
markers_list <- markers_list[reordered_levels_63]
saveRDS(markers_list, path_to_save_markers)
openxlsx::write.xlsx(
x = markers_list,
file = here::here("results/tables/markers/markers_annotated_clusters_patient_63.xlsx")
)
sessionInfo()
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=es_ES.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 tidyverse_1.3.1 ggpubr_0.4.0 ggplot2_3.3.3 SeuratObject_4.0.2 Seurat_4.0.3 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6 igraph_1.2.6 lazyeval_0.2.2 splines_4.0.4 crosstalk_1.1.1 listenv_0.8.0 scattermore_0.7 digest_0.6.27 htmltools_0.5.1.1 fansi_0.5.0 magrittr_2.0.1 tensor_1.5 cluster_2.1.1 ROCR_1.0-11 limma_3.46.0 openxlsx_4.2.3 globals_0.14.0 modelr_0.1.8 matrixStats_0.59.0 spatstat.sparse_2.0-0 colorspace_2.0-1 rvest_1.0.0 ggrepel_0.9.1 haven_2.4.1 xfun_0.23 crayon_1.4.1 jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-10 zoo_1.8-9 glue_1.4.2 polyclip_1.10-0 gtable_0.3.0 leiden_0.3.8 car_3.0-10 future.apply_1.7.0 abind_1.4-5 scales_1.1.1 DBI_1.1.1 rstatix_0.7.0 miniUI_0.1.1.1 Rcpp_1.0.6 viridisLite_0.4.0 xtable_1.8-4 reticulate_1.20 spatstat.core_2.1-2 foreign_0.8-81 DT_0.18 htmlwidgets_1.5.3 httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
## [55] ica_1.0-2 farver_2.1.0 pkgconfig_2.0.3 sass_0.4.0 uwot_0.1.10 dbplyr_2.1.1 deldir_0.2-10 here_1.0.1 utf8_1.2.1 labeling_0.4.2 tidyselect_1.1.1 rlang_0.4.11 reshape2_1.4.4 later_1.2.0 munsell_0.5.0 cellranger_1.1.0 tools_4.0.4 cli_2.5.0 generics_0.1.0 broom_0.7.7 ggridges_0.5.3 evaluate_0.14 fastmap_1.1.0 yaml_2.2.1 goftest_1.2-2 fs_1.5.0 knitr_1.33 fitdistrplus_1.1-5 zip_2.2.0 RANN_2.6.1 pbapply_1.4-3 future_1.21.0 nlme_3.1-152 mime_0.10 xml2_1.3.2 rstudioapi_0.13 compiler_4.0.4 plotly_4.9.4 curl_4.3.1 png_0.1-7 ggsignif_0.6.2 spatstat.utils_2.2-0 reprex_2.0.0 bslib_0.2.5.1 stringi_1.6.2 highr_0.9 RSpectra_0.16-0 lattice_0.20-41 Matrix_1.3-4 vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.0 BiocManager_1.30.15 spatstat.geom_2.1-0
## [109] lmtest_0.9-38 jquerylib_0.1.4 RcppAnnoy_0.0.18 data.table_1.14.0 cowplot_1.1.1 irlba_2.3.3 httpuv_1.6.1 patchwork_1.1.1 R6_2.5.0 bookdown_0.22 promises_1.2.0.1 KernSmooth_2.23-18 gridExtra_2.3 rio_0.5.26 parallelly_1.26.0 codetools_0.2-18 MASS_7.3-53.1 assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2 sctransform_0.3.2 mgcv_1.8-36 parallel_4.0.4 hms_1.1.0 grid_4.0.4 rpart_4.1-15 rmarkdown_2.8 carData_3.0-4 Rtsne_0.15 shiny_1.6.0 lubridate_1.7.10